The Utility Rate Database (URDB) is a free storehouse of rate structure information from utilities in the United States. Here, you can search for your utilities and rates to find out exactly how you are charged for your electric energy usage. Understanding this information can help reduce your bill, for example, by running your appliances during off-peak hours (times during the day when electricity prices are less expensive) and help you make more informed decisions regarding your energy usage.

Rates are also extremely important to the energy analysis community for accurately determining the value and economics of distributed generation such as solar and wind power. In the past, collecting rates has been an effort duplicated across many institutions. Rate collection can be tedious and slow, however, with the introduction of the URDB, OpenEI aims to change how analysis of rates is performed. The URDB allows anyone to access these rates in a computer-readable format for use in their tools and models. OpenEI provides an API for software to automatically download the appropriate rates, thereby allowing detailed economic analysis to be done without ever having to directly handle complex rate structures. Essentially, rate collection and processing that used to take weeks or months can now be done in seconds!

NREL’s System Advisor Model (formerly Solar Advisor Model or SAM), currently has the ability to communicate with the OpenEI URDB over the internet. SAM can download any rate from the URDB directly into the program, thereby enabling users to conduct detailed studies on various power systems ranging in size from a small residential rooftop solar system to large utility scale installations. Other applications available at NREL, such as OpenPV and IMBY, will also utilize the URDB data.

Upcoming features include better support for entering net metering parameters, maps to summarize the data, geolocation capabilities, and hundreds of additional rates!

This dataset, compiled by NREL using data from ABB, the Velocity Suite (http://energymarketintel.com/) and the U.S. Energy Information Administration dataset 861 (http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database (https://openei.org/apps/USURDB/).

Abstract

This dataset, compiled by NREL using data from ABB, the Velocity Suite and the U.S. Energy Information Administration dataset 861, provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database.

This dataset, compiled by NREL using data from Ventyx and the U.S. Energy Information Administration dataset 861, provides average residential, commercial and industrial electricity rates by zip code for both investor owned utilities (IOU) and non-investor owned utilities in Utah. Note: the file includes average rates for each utility, but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database. A more recent version of this data is also available through the NREL Utility Rate API with more search options. This data was released by NREL/Ventyx in February 2011.

The data package provides average residential, commercial, and industrial electricity rates by zip code for both investor-owned utilities (IOU) and non-investor owned utilities. The datasets include information such as peak load, generation, electric purchases, sales, revenues, customer counts and demand-side management programs, green pricing and net metering programs, and distributed generation capacity.

The table Non IOU Zipcodes 2019 is part of the dataset Open Energy Data Initiative: U.S. Electric Utility Consumption and Rates ***, available at https://redivis.com/datasets/w5hb-cs453cj2k. It contains 36494 rows across 9 variables.

NREL has assembled a list of U.S. retail electricity tariffs and their associated demand charge rates for the Commercial and Industrial sectors. The data was obtained from the Utility Rate Database. Keep the following information in mind when interpreting the data: (1) These data were interpreted and transcribed manually from utility tariff sheets, which are often complex. It is a certainty that these data contain errors, and therefore should only be used as a reference. Actual utility tariff sheets should be consulted if an action requires this type of data. (2) These data only contains tariffs that were entered into the Utility Rate Database. Since not all tariffs are designed in a format that can be entered into the Database, this list is incomplete - it does not contain all tariffs in the United States. (3) These data may have changed since this list was developed (4) Many of the underlying tariffs have additional restrictions or requirements that are not represented here. For example, they may only be available to the agricultural sector or closed to new customers. (5) If there are multiple demand charge elements in a given tariff, the maximum demand charge is the sum of each of the elements at any point in time. Where tiers were present, the highest rate tier was assumed. The value is a maximum for the year, and may be significantly different from demand charge rates at other times in the year. Utility Rate Database: https://openei.org/wiki/Utility_Rate_Database

A comprehensive dataset of average residential, commercial, and combined electricity rates in cents per kWh for all 50 U.S. states.

Spreadsheet with water rate information, including average residential monthly water bill, average residential monthly wastewater bill and water and wastewater rate at a consumption level of 5,000 gallons, volumetric and fixed rates, rate type, billing cycles, number and width of rate blocks, for each utility for which data was available in the northeastern Illinois region. Data is presented in original and standardized formats, each with a separate key. Fiscal year 2009 ran from July 2008 to June 2009.FY2015 DataFY2017 DataFY2019 DataFY2021 Data

One in a series of studies on customer response to utility regulatory pricing in early 1975, the North Carolina demonstration project was carried out by the North Carolina Utilities Commission (NCUC) under a cooperative agreement with the Department of Energy. The participating utilities were Blue Ridge Electric Membership Corporation (BREM or BR) and Carolina Power and Light Company (CP&L). Research Triangle Institute (RTI) provided the research and analysis support to the project, and ICF, Inc. consulted on the time-of-use rate design. The experiment lasted from 1977 to 1979 and involved residential customers of the two participating utilities. Four sets of data resulted from the demonstration: questionnaire survey data from the customers, summary demographic information, utility load reports, and customer usage records. (No weather data were collected.) Three of the sets are available in this data collection. Parts 2 and 3 contain 28 days of data, including hourly data. They also contain identifying information that links their data to the pertinent customer/participant's demographic data in Part 1.

One in a series of studies on customer response to utility regulatory pricing in early 1975, the Rhode Island demonstration project was carried out by the Federal Energy Administration (FEA) and the Blackstone Valley Electric Company from 1977 to 1978. The study was originally titled the Rhode Island Time of Use Rate Experiment and was conducted to generate and analyze data on the effects of peak-load pricing on residential electric consumption. The experimental design featured a seasonally differentiated time of day rate. Three sets of data resulted from the demonstration: questionnaire survey of the customers, summary demographic information, and customer usage records. All three sets are available in this data collection. Part 1 contains post-experimental customer survey responses. Part 5 contains hourly electricity consumption data for the 28 days of the experiment, along with identifying information that links such data to the pertinent customer/participant's demographic data in Part 2.

One in a series of studies on customer response to utility regulatory pricing in early 1975, the Ohio demonstration project occurred in 1976 and 1977 and was carried out by the Federal Energy Administration (FEA), Public Utilities Commission, Dayton Power and Light, Toledo Edison, Buckeye Power, and the Motorola Corporation. The study was originally titled the Ohio Electric Demonstration Project and was an experiment to generate and analyze data on the effects of peak-load pricing on residential electric consumption. The experimental design featured a seasonally differentiated time of day rate. A strike by the Dayton Power and Light employees from January to April of 1977 had a negative impact on the data collection. Five sets of data resulted from the demonstration: questionnaire survey of the customers, summary demographic information, utility load reports, weather data, and customer usage records. All five sets are available in this data collection. Parts 3-5 each contain 28 days of data, with Parts 3 and 5 including hourly data. Parts 3-5 each also contain identifying information that links their data to the pertinent customer/participant's demographic data in Part 2.

These data underpin an analysis of the time-sensitive impacts of energy efficiency and flexibility measures in the U.S. building sector using Scout (scout.energy.gov), a reproducible and granular model of U.S. building energy use developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office.

The analysis applies sub-annual adjustments to U.S. baseline building energy use, cost, and emissions in order to characterize how these metrics vary across hour of the day, season, and geographic region in the U.S. building sector. These adjustments are based on daily energy load, price, and emissions shapes from various data sources and are used to re-apportion baseline energy, cost, and emissions totals from EIA's Annual Energy Outlook (AEO) Reference Case projections across all hours of a year. The resulting sub-annual baselines are specified by building sector, end use, region, and season and can be used in analyses of building efficiency and flexibility measures to quantify their time-sensitive impacts at the national scale. Analyses of these data demonstrate that energy efficiency measures continue to show strong value under a time-sensitive framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency.

The data uploaded below include CSV files that show hourly energy use, cost, and emissions totals for the U.S. building sector as well as by end-use, region, and season. An additional CSV includes residential and commercial price intensities (USD/quad) for all hours of the day based on different time-of-use (TOU) rate data from the U.S. Utility Rate Database (URDB). Further detail on each of these CSVs is given below:

'TSV_baseline_totals.csv': this file shows hourly total energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030. It presents these estimates in Quads (source), Quads (site), and TWh (site). For the cost totals, it presents two estimates for each year and building sector, including one using the median TOU rate from the URDB and one using the average retail rate for the corresponding building sector. For converting source energy to site, total delivered electricity and electricity-related losses data for the residential and commercial sector are drawn from AEO Summary Table A2.

'TSV_baseline_end-use.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030 broken out by building end-use. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.

'TSV_baseline_region.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential space heating and cooling end uses in 2018 and 2030 for each American Institute of Architects (AIA) climate zone. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.

'TSV_baseline_region_season.csv': this file shows a similar disaggregation of the data as ‘TSV_baseline_region.csv’, but it further disaggregates results by season. The seasonal definitions are as follows: 'intermediate' (October to November; March to April), 'winter' (November to February), and 'summer' (May to September).

'TSV_annual_price_intensities.csv': this file presents annual hourly price intensities for the commercial and residential building sectors in 2018 and 2030 based on different TOU rate data from the URDB. Three different rate structures are included for each building sector, and these are the 5th, 50th, and 95th percentile of all existing commercial and residential TOU rates in the URDB in terms of their peak to off-peak price ratio.

Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by introducing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized in states with net metering policies facing the challenge of above-average electric utility rates.This dataset is intended to allow readers to reproduce and customize the analysis performed in this work to their benefit. Suggested modifications include: location, household load profile, rate tariff structure, and renewable energy system design.

One in a series of studies on customer response to utility regulatory pricing in early 1975, the Oklahoma demonstration project was carried out by the Federal Energy Administration (FEA), the city of Edmond, Central State University, C.H. Guernsey and Company, and the Center for Economic and Management Research at the University of Oklahoma. The project spanned one year from 1977 to 1978. The study, also titled the Electricity Rate Study or Electric Demonstration Project, was an experiment to generate and analyze data on the effects of peak-load pricing on residential electricity consumption. The experimental design featured four non-traditional rate structures: time of day rates, flat rates, seasonal rates, and a combination of seasonal and time-of-day rates. Four sets of data resulted from the demonstration: questionnaire survey data from customers, utility load reports, weather data, and customer usage records. (No summary demographic information exists.) Only data from the post-experimental customer questionnaire survey are available in this data collection.

United States Electric Retail Price: Sold by Electric Utilities: Avg: Residential data was reported at 12.890 USD/kWh in 2017. This records an increase from the previous number of 12.550 USD/kWh for 2016. United States Electric Retail Price: Sold by Electric Utilities: Avg: Residential data is updated yearly, averaging 7.565 USD/kWh from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 12.890 USD/kWh in 2017 and a record low of 2.200 USD/kWh in 1970. United States Electric Retail Price: Sold by Electric Utilities: Avg: Residential data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s United States – Table US.P011: Electricity Price.

Summary of utility base rate changes since 1998 for major NYS utilities.

One in a series of studies on customer response to utility regulatory pricing in early 1975, the Arkansas demonstration project was carried out by the Federal Energy Administration (FEA), the Arkansas Public Service Commission, and Torche Ross and Company, spanning 12 months from February 1976 to January 1977. The study was originally titled the Arkansas Demand Management Study and was an experiment to generate and analyze data on the effects of peak-load pricing on residential electricity consumption. The experimental design featured a time of day peak-load pricing test as well as a seasonal pricing test. Five sets of data resulted from the demonstration: questionnaire survey data from the customers, summary demographic information, utility load reports, weather data, and customer usage records. All five sets are available in this data collection. The questionnaire survey data in Part 1 consists of information gathered from a post experimental survey that includes both control and experimental customers. Parts 3-5 each contain 28 days of data, with Parts 3 and 5 including hourly data. Parts 3-5 also contain identifying information that links their data to the pertinent customer/participant's demographic data in Part 2.

This spreadsheet contains information reported by over 200 investor-owned utilities to the Federal Energy Regulatory Commission in the annual filing FERC Form 1 for the years 1994-2019. It contains 1) annual capital costs for new transmission, distribution, and administrative infrastructure; 2) annual operation and maintenance costs for transmission, distribution, and utility business administration; 3) total annual MWh sales and sales by customer class; 4) annual peak demand in MW; and 5) total customer count and the number of customers by class. Annual spending on new capital infrastructure is read from pages 204 to 207 of FERC Form 1, titled Electric Plant in Service. Annual transmission capital additions are recorded from Line 58, Column C - Total Transmission Plant Additions. Likewise, annual distribution capital additions are recorded from Line 75, Column C - Total Distribution Plant Additions. Administrative capital additions are recorded from Line 5, Column C - Total Intangible Plant Additions, and Line 99, Column C - Total General Plant Additions. Operation and maintenance costs associated with transmission, distribution, and utility administration are read from pages 320 to 323 of FERC Form 1, titled Electric Operation and Maintenance Expenses. Annual transmission operation and maintenance are recorded from Line 99, Column B - Total Transmission Operation Expenses for Current Year, and Line 111, Column B - Total Transmission Maintenance Expenses for Current Year. Likewise, annual distribution operation and maintenance costs are recorded from Line 144, Column B - Total Distribution Operation Expenses, and Line 155, Column B - Total Distribution Maintenance Expenses. Administrative operation and maintenance costs are recorded from: Line 164, Column B - Total Customers Accounts Expenses; Line 171, Column B - Total Customer Service and Information Expenses; Line 178, Column B - Total Sales Expenses; and Line 197, Column B - Total Administrative and General Expenses. The annual peak demand in MW over the year is read from page 401, titled Monthly Peaks and Output. The monthly peak demand is listed in Lines 29 to 40, Column D. The maximum of these monthly reports during each year is taken as the annual peak demand in MW. The annual energy sales and customer count data come from page 300, Electric Operating Revenues. The values are provided in Line 2 - Residential Sales, Line 4 - Commercial Sales, Line 5 - Industrial Sales, and Line 10 - Total Sales to Ultimate Consumers. More information about the database is available in an associated report published by the University of Texas at Austin Energy Institute: https://res1live-energy-instituted-o-tpantheonsited-o-tio.vcapture.xyz/sites/default/files/UTAustin_FCe_TDA_2016.pdf Also see an associated paper published in the journal Energy Policy: Fares, Robert L., and Carey W. King. "Trends in transmission, distribution, and administration costs for US investor-owned electric utilities." Energy Policy 105 (2017): 354-362. https://res1doid-o-torg.vcapture.xyz/10.1016/j.enpol.2017.02.036 All data come from the Federal Energy Regulatory Commission FERC Form 1 Database available in Microsoft Visual FoxPro Format: https://res1wwwd-o-tfercd-o-tgov.vcapture.xyz/docs-filing/forms/form-1/data.asp

Electric power selling price index (EPSPI). Monthly data are available from January 1981. The table presents data for the most recent reference period and the last four periods. The base period for the index is (2014=100).